In light of the above issues, we develop a hyperedge-based graph neural network, namely HGNN, for CR. Specifically, (1) to model the relationships among learners, we treat learners (i.e., hyperedges) as the sets of courses in a hypergraph, and convert the task of learning learners' ...
【2019/ICML】DAG-GNN: DAG Structure Learning with Graph Neural Networks 原文链接:https://dreamhomes.github.io/posts/202101041501.html 文章链接:https://arxiv.org/abs/1904.10098 源码链接:https://github.com/fishmoon1234/DAG-GNN TL;DR 论文中提出一种新的DAG编码架构 DAG-GNN,其实模型的本质就是一...
Graph Neural Network, which aggre-gates the topological information of the neighbourhoods of each node in a graph to imple-ment graph/network embedding, has attracted wide attention. With the explosive growth of information, large amou...
Hyper-SAGNN: a self-attention based graph neural network for hypergraphs,ICLR(2020)Ruochi Zhang,Yuesong Zou,Jian Ma 这篇文章针对的是hypergraph,并且使用的数据是scHi-C(single cell Hi-C),两种感觉都不是经常能见到的名词凑一起了。 本文处理的对象是Hi-C数据集,但在实验里也给出了其他可供参考的数据...
In addition, prior works overlook the rich structural information inherent in KG, which consists of higher-order relations and can further alleviate the impact of data this http URL this end, we propose a Hyper-Relational Knowledge Graph Neural Network (HKGNN) model. In HKGNN, a Hyper-...
2021. Distributed hybrid CPU and GPU training for graph neural networks on billion-scale graphs. Retrieved from arxiv.org/abs/2112.1534. [194]Zhu Xiaojin and Ghahramani Zoubin. 2002. Learning from labeled and unlabeled data with label propagation. Technical Report....
等变GNN (Equivariant Graph Neural Network): 在三维空间中,蛋白质的结构可能会发生旋转或反射。等变 GNN 作为核心网络层,设计成能够识别并保持这种旋转不变性的结构,即无论蛋白质图形如何旋转,网络的输出对于相同的蛋白质结构都应该是一致的。 节点嵌入 (Node Embedding): ...
570(机器学习编程篇4)10.3 GraphX - 3 17:37 571(机器学习编程篇5)1.1 Scala基础与实践(上) - 1 18:52 572(机器学习编程篇5)1.1 Scala基础与实践(上) - 2 19:02 573(机器学习编程篇5)1.1 Scala基础与实践(上) - 3 18:52 574(机器学习编程篇5)1.2 Scala基础与实践(中) - 1 17:38 575(机器...
题目: HYPER-SAGNN: A Self-Attention Based Graph Neural Network for Hypergraphs 作者: Ruochi Zhang, Yuesong Zou, Jian Ma Paper:Link(ICLR 2020) 机构: CMU, Tsinghua Code:Source code Summary: 本文提出了一种超边预测模型,其主体为基于self-attention的超图图神经网络模型,可以应对具有不同大小的超边结构...
The inset graph illustrates the corresponding area under the GCC curve (GCC-AUC, AUC for short) of each line of the number of removed nodes (hyperedges) versus the GCC size of the network. The calculation of AUC comprises summing the GCC values for each curve at each step until the GCC...